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COMP 204 Assignment

Submit one Python program on MyCourses, which should contain all your functions. The file should be calledpy
Write your name and student ID at the top of theprogram
For each question, complete the relevant function(s). Do not change the functions’ name or arguments. You can create additional functions if
Do not use any modules or any pieces of code you did not write
For each question, your function will be automatically tested on a variety of test cases, which will account for 75% of the
For each question, 25%of the mark will be assigned by the TA based on (i) Re-‐use of functions as appropriate; (ii) appropriate naming of variables; (iii) commenting of your program; (iv) simplicity of your program (simpler = better).
Importantnotes: This assignment focuses a lot on software re-‐us You will be writing several functions. Some of those functions should call other functions in order to re-‐use the code already written. Make sure to re-‐use your functions as much as needed!

Background: Your task in this assignment is to write a program that will help doctors make disease diagnostics based on a patient’s symptom. This will be achieved by comparing a new patient’s symptoms to a database of patients containing both their symptoms and their diagnostic. Given a patient X with a certain set of symptoms, your diagnostic help to the doctor will be obtained by identifying, among the patients in the database, those whose symptoms are most similar to those of patient X. This is actually a commonly used approach in artificial intelligence, called k-‐nearest neighbors classification, although everything you need to know about k-‐nearest neighbors classification is contained in this assignment.

Download medical_diagnostic.py from MyCourses.

The file contains several functions that you will need to complete. It also contains the my_test() function, which calls each of the functions you will write. The expected (correct) output for several examples is given in the file my_test_output.txt. Use this to make sure that you understand what every function is expected to do and to test your own code. Note: You will not be able to run the my_test() function before you complete all the functions it calls. In order you to test your functions one at a time, comment out the portions of the my_test() function that call functions you have not yet written.

Data representation:

Each patient is identified using an integer identifier (e.g.56374).
Each symptoms is a string such as “headache” or“fever”.
Each diagnostic is a string such as “cold” or“meningitis”
Symptoms for a given patient are stored in a tuple of two sets: the first set contains the symptoms that are present in the patient; the second set contains the symptoms that are observed not to be present (i.e.absent).

Note that for a given patient, information may be missing about whether or not a patient has a symptom. For example, in the example above we don’t know if the patient has a sore

The set of symptoms of all the patients in our database is represented using a dictionary, whose keys are the patient identifiers, and values are the tuples of symptoms. Forexample:

Good to know before you start:

o Suppose you have a list of tuples and you want to sort the list based on the elements at a particular index of the tuple. Example: sort a list of tuples based on the values at index 1 of the tuples.

The List type has a sort() function, but how to tell the sort function what element of the tuple to look at? First, define a small function that takes an as argument a tuple and returns element at index 1 of the tuple:

def getKey1(item): return item[1]

Then, tell the sort() function to use the getKey1() function to select the keys to base the sorting upon. This is done by passing the getKey1 function as a keyword argument to the sort function. (Yes, functions can be passed as arguments to other functions!).

someList.sort(key=getKey1)

To sort in reverse order:

someList.sort(key=getKey1, reverse=True)

Question 1 (10 Points)

Complete the symptom_similarity() function, which measures the similarity between the symptoms of two patients. See below for an explanation of how the similarity is computed, and see my_test() function for examples.

def symptom_similarity(my_symptoms, other_symptoms): “””

Args:

my_symptoms: tuple of a set of symptoms present and a set of symptoms absent other_symptoms: tuple of a set of symptoms present and set symptoms absent

Returns:

common_present + common_absent – common_present_absent – common_absent_present where common_present is the number of symptoms present in both patients common_absent is the number of symptoms absent in both patients common_present_absent is the number of symptoms present in my_patient and absent in other_patient

common_absent_present is the number of symptoms absent in my_patient and present in other_patient

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